This PEP describes an abstraction of asynchronous IO for the Python
standard library.

The goal is to reach an abstraction that can be implemented by many
different asynchronous IO backends and provides a target for library
developers to write code portable between those different backends.

Unfortunately, each of these options has its downsides, which this PEP
tries to address.

Despite having been part of the Python standard library for a long
time, the asyncore module suffers from fundamental flaws following
from an inflexible API that does not stand up to the expectations of a
modern asynchronous networking module.

Moreover, its approach is too simplistic to provide developers with
all the tools they need in order to fully exploit the potential of
asynchronous networking.

The most popular solution right now used in production involves the
use of third party libraries. These often provide satisfactory
solutions, but there is a lack of compatibility between these
libraries, which tends to make codebases very tightly coupled to the
library they use.

This current lack of portability between different asynchronous IO
libraries causes a lot of duplicated effort for third party library
developers. A sufficiently powerful abstraction could mean that
asynchronous code gets written once, but used everywhere.

An eventual added goal would be for standard library implementations
of wire and network protocols to evolve towards being real protocol
implementations, as opposed to standalone libraries that do everything
including calling recv() blockingly. This means they could be
easily reused for both synchronous and asynchronous code.

Transports provide a uniform API for reading bytes from and writing
bytes to different kinds of connections. Transports in this PEP are
always ordered, reliable, bidirectional, stream-oriented two-endpoint
connections. This might be a TCP socket, an SSL connection, a pipe
(named or otherwise), a serial port... It may abstract a file
descriptor on POSIX platforms or a Handle on Windows or some other
data structure appropriate to a particular platform. It encapsulates
all of the particular implementation details of using that platform
data structure and presents a uniform interface for application
developers.

Transports talk to two things: the other side of the connection on one
hand, and a protocol on the other. It's a bridge between the specific
underlying transfer mechanism and the protocol. Its job can be
described as allowing the protocol to just send and receive bytes,
taking care of all of the magic that needs to happen to those bytes to
be eventually sent across the wire.

The primary feature of a transport is sending bytes to a protocol and
receiving bytes from the underlying protocol. Writing to the
transport is done using the write and write_sequence methods.
The latter method is a performance optimization, to allow software to
take advantage of specific capabilities in some transport mechanisms.
Specifically, this allows transports to use writev[4] instead of write[5]
or send[6], also known as scatter/gather IO.

A transport can be paused and resumed. This will cause it to buffer
data coming from protocols and stop sending received data to the
protocol.

A transport can also be closed, half-closed and aborted. A closed
transport will finish writing all of the data queued in it to the
underlying mechanism, and will then stop reading or writing data.
Aborting a transport stops it, closing the connection without sending
any data that is still queued.

Further writes will result in exceptions being thrown. A half-closed
transport may not be written to anymore, but will still accept
incoming data.

Protocols are probably more familiar to new users. The terminology is
consistent with what you would expect from something called a
protocol: the protocols most people think of first, like HTTP, IRC,
SMTP... are all examples of something that would be implemented in a
protocol.

The shortest useful definition of a protocol is a (usually two-way)
bridge between the transport and the rest of the application logic. A
protocol will receive bytes from a transport and translates that
information into some behavior, typically resulting in some method
calls on an object. Similarly, application logic calls some methods
on the protocol, which the protocol translates into bytes and
communicates to the transport.

One of the simplest protocols is a line-based protocol, where data is
delimited by \r\n. The protocol will receive bytes from the
transport and buffer them until there is at least one complete line.
Once that's done, it will pass this line along to some object.
Ideally that would be accomplished using a callable or even a
completely separate object composed by the protocol, but it could also
be implemented by subclassing (as is the case with Twisted's
LineReceiver). For the other direction, the protocol could have a
write_line method, which adds the required \r\n and passes the
new bytes buffer on to the transport.

This PEP suggests a generalized LineReceiver called
ChunkProtocol, where a "chunk" is a message in a stream, delimited
by the specified delimiter. Instances take a delimiter and a callable
that will be called with a chunk of data once it's received (as
opposed to Twisted's subclassing behavior). ChunkProtocol also
has a write_chunk method analogous to the write_line method
described above.

This separation between protocol and transport often confuses people
who first come across it. In fact, the standard library itself does
not make this distinction in many cases, particularly not in the API
it provides to users.

It is nonetheless a very useful distinction. In the worst case, it
simplifies the implementation by clear separation of concerns.
However, it often serves the far more useful purpose of being able to
reuse protocols across different transports.

Consider a simple RPC protocol. The same bytes may be transferred
across many different transports, for example pipes or sockets. To
help with this, we separate the protocol out from the transport. The
protocol just reads and writes bytes, and doesn't really care what
mechanism is used to eventually transfer those bytes.

This also allows for protocols to be stacked or nested easily,
allowing for even more code reuse. A common example of this is
JSON-RPC: according to the specification, it can be used across both
sockets and HTTP [1]. In practice, it tends to be primarily
encapsulated in HTTP. The protocol-transport abstraction allows us to
build a stack of protocols and transports that allow you to use HTTP
as if it were a transport. For JSON-RPC, that might get you a stack
somewhat like this:

Consumers consume bytes produced by producers. Together with
producers, they make flow control possible.

Consumers primarily play a passive role in flow control. They get
called whenever a producer has some data available. They then process
that data, and typically yield control back to the producer.

Consumers typically implement buffers of some sort. They make flow
control possible by telling their producer about the current status of
those buffers. A consumer can instruct a producer to stop producing
entirely, stop producing temporarily, or resume producing if it has
been told to pause previously.

Producers are modeled after the IPushProducer[7] interface found in
Twisted. Although there is an IPullProducer[8] as well, it is on the
whole far less interesting and therefore probably out of the scope of
this PEP.

Although producers can be told to stop producing entirely, the two
most interesting methods they have are pause and resume.
These are usually called by the consumer, to signify whether it is
ready to process ("consume") more data or not. Consumers and
producers cooperate to make flow control possible.

In addition to the Twisted IPushProducer[7] interface, producers have a
half_register method which is called with the consumer when the
consumer tries to register that producer. In most cases, this will
just be a case of setting self.consumer = consumer, but some
producers may require more complex preconditions or behavior when a
consumer is registered. End-users are not supposed to call this
method directly.

Generators have been suggested as way to implement producers.
However, there appear to be a few problems with this.

First of all, there is a conceptual problem. A generator, in a sense,
is "passive". It needs to be told, through a method call, to take
action. A producer is "active": it initiates those method calls. A
real producer has a symmetric relationship with its consumer. In the
case of a generator-turned-producer, only the consumer would have a
reference, and the producer is blissfully unaware of the consumer's
existence.

This conceptual problem translates into a few technical issues as
well. After a successful write method call on its consumer, a
(push) producer is free to take action once more. In the case of a
generator, it would need to be told, either by asking for the next
object through the iteration protocol (a process which could block
indefinitely), or perhaps by throwing some kind of signal exception
into it.

This signaling setup may provide a technically feasible solution, but
it is still unsatisfactory. For one, this introduces unwarranted
complexity in the consumer, which now not only needs to understand how
to receive and process data, but also how to ask for new data and deal
with the case of no new data being available.

This latter edge case is particularly problematic. It needs to be
taken care of, since the entire operation is not allowed to block.
However, generators can not raise an exception on iteration without
terminating, thereby losing the state of the generator. As a result,
signaling a lack of available data would have to be done using a
sentinel value, instead of being done using th exception mechanism.

Last but not least, nobody produced actually working code
demonstrating how they could be used.